The invention relates to the field of magnetic resonance imaging (MRI).
MRI is the method of choice for clinical brain imaging, as it involves no exposure to ionizing radiation and provides high quality imaging for soft tissue. Conventional clinical MRI scan may last up to one hour and consists of multiple imaging contrasts of the same region. Radiologists may to detect subtle abnormalities, such as a developing tumor, by comparing multiples images of the same region and examining the variations in contrast of the different tissue types. However, the scanning procedure in MRI is time-consuming, resulting in patients spending prolonged times inside the machine.
A previously acquired image may serve as a reference image to shorten acquisition time and/or to improve Signal to Noise Ratio (SNR), by exploiting a degree of similarity between the reference and acquired image. For example, similarity may exist between adjacent slices in high resolution MRI, between various contrasts in the same scans, and between different scans of the same patient.
MRI data is typically sampled in the spatial Fourier transform (‘k-space’) of the object under investigation. Due to constraints in the implementation of the k-space trajectory that controls the sampling pattern, the k-space may be sampled below the minimum rate at which a signal can be sampled without introducing errors, the Nyquist rate. For example, acquisition duration, scheme, smoothness of gradients can pose such sampling constraints. Prior assumptions on the nature of the data may be used to reconstruct higher quality images from the sparsely sampled images, such as to overcome any imaging artefacts introduced due to insufficient sampling.
Furthermore, MR images are highly compressible, and the image reconstruction problem may be formalized as an l1 minimization problem. These sparse MRI reconstruction approaches fall into two general categories: single- and multiple-image sparsity-based reconstruction.
The first category exploits the sparsity of a single MRI image in some transform domain. Wavelet transforms are widely used as a sparsifying transform for brain images, whereas total variation (TV) are generally used for angio-MRI. Other approaches focus on sparsifying transform learning techniques, or use a dictionary developed exclusively for MRI. However, these approaches may suffer from artefacts in cases of severe undersampling.
The second category exploits the similarity within a series of MRI images. In dynamic imaging, MRI images are acquired at a high frame rate and sparsity may be introduced by applying a Fourier transformation along the temporal dimension, assuming that only parts of the field-of-view (FOV) change at a high temporal rate, as described in U. Gamper et al., “Compressed sensing in dynamic MRI,” Magnetic Resonance in Medicine, vol. 59, no. 2, pp. 365-373, 2008. Other techniques represent dynamic MRI as a superposition of a low-rank background component and a sparse dynamic component. In multiple-contrast MRI, structural similarity is assumed between contrasts, and sparsity is enforced on the difference between gradient images having different imaging contrast, as described in Berkin Bilgic, Vivek K Goyal, and Elfar Adalsteinsson, “Multi-contrast reconstruction with bayesian compressed sensing,” Magnetic Resonance in Medicine, vol. 66, no. 6, pp. 1601-1615, 2011.
Since similarity between multiple images takes different forms, a different sparsity-based reconstruction is typically used for different MRI applications, exploiting the specific sparsity characteristics of each application. However, assuming substantial similarity between the images in the series, in the image domain or in some transform domain may not always be valid and may lead to undesired reconstruction results.
Other high resolution MRI applications suffer from low Signal to Noise Ratio (SNR) and require multiple scanning repetitions to yield an adequate SNR, which may double or triple the total scanning time. Improvements to the hardware and/or the acquisition process may improve the SNR in these cases.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.